diff --git "a/notebooks/02_model_testing.ipynb" "b/notebooks/02_model_testing.ipynb"
--- "a/notebooks/02_model_testing.ipynb"
+++ "b/notebooks/02_model_testing.ipynb"
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "setup-imports",
    "metadata": {},
    "outputs": [
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "32778fbb-7d4f-4dd8-87c8-53d9ed77ae69",
    "metadata": {},
    "outputs": [
@@ -226,7 +226,7 @@
       "applications while maintaining human oversight and ethical standards.\n",
       "\n",
       "Metadata:\n",
-      "  - Processing time: 0.0330s\n",
+      "  - Processing time: 0.0332s\n",
       "  - Summary length: 78 words\n",
       "  - Compression: 40.00%\n",
       "\n",
@@ -237,7 +237,7 @@
       "Summary: Artificial intelligence has revolutionized modern technology in unprecedented ways. Machine learning algorithms enable computers to learn from vast amounts of data. Deep learning neural networks, inspired by the human brain, can recognize patterns in images, understand natural language, and even generate creative content.\n",
       "\n",
       "Metadata:\n",
-      "  - Processing time: 11.2542s\n",
+      "  - Processing time: 8.0204s\n",
       "  - Summary length: 43 words\n",
       "  - Compression: 22.05%\n",
       "\n",
@@ -248,7 +248,7 @@
       "Summary: Machine learning algorithms enable computers to learn from vast amounts of data without explicit programming.<n>Deep learning neural networks, inspired by the human brain, can now recognize patterns in images, understand natural language, and even generate creative content.<n>Computer vision technology allows machines to interpret and understand visual information from the world.\n",
       "\n",
       "Metadata:\n",
-      "  - Processing time: 15.3631s\n",
+      "  - Processing time: 7.8367s\n",
       "  - Summary length: 50 words\n",
       "  - Compression: 25.64%\n",
       "\n",
@@ -314,7 +314,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:utils.evaluator:Evaluator initialized with rouge library\n"
+      "INFO:absl:Using default tokenizer.\n",
+      "INFO:utils.evaluator:Evaluator initialized with rouge_score library\n"
      ]
     },
     {
@@ -325,9 +326,9 @@
       "Evaluating against reference summary...\n",
       "\n",
       "   Model  ROUGE-1  ROUGE-2  ROUGE-L  Time (s)  Words  Compression\n",
-      "TextRank 0.254902 0.067227 0.235294  0.032988     78     0.400000\n",
-      "    BART 0.101266 0.000000 0.101266 11.254168     43     0.220513\n",
-      " PEGASUS 0.095238 0.000000 0.095238 15.363073     50     0.256410\n"
+      "TextRank 0.338710 0.081967 0.209677  0.033205     78     0.400000\n",
+      "    BART 0.298851 0.070588 0.206897  8.020402     43     0.220513\n",
+      " PEGASUS 0.285714 0.083333 0.204082  7.836727     50     0.256410\n"
      ]
     }
    ],
@@ -377,13 +378,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "4c130551-5528-4246-9e01-97c5c660622d",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 1500x1000 with 4 Axes>"
       ]
@@ -431,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "871b5e06-91b8-44da-8432-ebc7657558e7",
    "metadata": {},
    "outputs": [
@@ -444,17 +445,17 @@
       "DETAILED ANALYSIS\n",
       "======================================================================\n",
       "\n",
-      "🏆 Best ROUGE-1 Score: TextRank\n",
-      "🏆 Best ROUGE-2 Score: TextRank\n",
+      " Best ROUGE-1 Score: TextRank\n",
+      " Best ROUGE-2 Score: PEGASUS\n",
       "⚡ Fastest Processing: TextRank\n",
       "\n",
-      "📊 Key Observations:\n",
-      "1. Quality: TextRank and TextRank provide best quality\n",
-      "2. Speed: TextRank is 465.7x faster\n",
+      " Key Observations:\n",
+      "1. Quality: TextRank and PEGASUS provide best quality\n",
+      "2. Speed: TextRank is 241.5x faster\n",
       "3. TextRank is extractive (uses original sentences)\n",
       "4. BART and PEGASUS generate new text (abstractive)\n",
       "\n",
-      "💡 Recommendations:\n",
+      " Recommendations:\n",
       "- Use TextRank for: Fast processing, resource-constrained environments\n"
      ]
     }
@@ -468,17 +469,17 @@
     "best_rouge2 = comparison_df.loc[comparison_df['ROUGE-2'].idxmax(), 'Model']\n",
     "fastest = comparison_df.loc[comparison_df['Time (s)'].idxmin(), 'Model']\n",
     "\n",
-    "print(f\"\\n🏆 Best ROUGE-1 Score: {best_rouge1}\")\n",
-    "print(f\"🏆 Best ROUGE-2 Score: {best_rouge2}\")\n",
+    "print(f\"\\n Best ROUGE-1 Score: {best_rouge1}\")\n",
+    "print(f\" Best ROUGE-2 Score: {best_rouge2}\")\n",
     "print(f\"⚡ Fastest Processing: {fastest}\")\n",
     "\n",
-    "print(\"\\n📊 Key Observations:\")\n",
+    "print(\"\\n Key Observations:\")\n",
     "print(f\"1. Quality: {best_rouge1} and {best_rouge2} provide best quality\")\n",
     "print(f\"2. Speed: {fastest} is {comparison_df['Time (s)'].max()/comparison_df['Time (s)'].min():.1f}x faster\")\n",
     "print(f\"3. TextRank is extractive (uses original sentences)\")\n",
     "print(f\"4. BART and PEGASUS generate new text (abstractive)\")\n",
     "\n",
-    "print(\"\\n💡 Recommendations:\")\n",
+    "print(\"\\n Recommendations:\")\n",
     "if fastest == \"TextRank\":\n",
     "    print(\"- Use TextRank for: Fast processing, resource-constrained environments\")\n",
     "if best_rouge1 in [\"BART\", \"PEGASUS\"]:\n",
@@ -496,9 +497,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:base] *",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "conda-base-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -510,7 +511,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.5"
+   "version": "3.13.9"
   }
  },
  "nbformat": 4,